3 citations · 4 across the 5 of their papers we have counts for
5 papers
Learning Input-Channel Permutation Equivariance for Multi-Channel Source Separation: Reducing Bleeding in Small Music Ensembles
Ruchi Pandey, Jaime Garcia-Martinez, Pablo Cabanas-Molero +5
Microphone bleed is a persistent challenge in small ensembles and orchestral recordings, where close microphones intended for individual instruments also capture leakage from nearb…
The Spheres Dataset: Multitrack Orchestral Recordings for Music Source Separation and Information Retrieval
Jaime Garcia-Martinez, David Diaz-Guerra, John Anderson +5
This paper introduces The Spheres dataset, multitrack orchestral recordings designed to advance machine learning research in music source separation and related MIR tasks within th…
SynthSOD: Developing an Heterogeneous Dataset for Orchestra Music Source Separation
Jaime Garcia-Martinez, David Diaz-Guerra, Archontis Politis +3
Recent advancements in music source separation have significantly progressed, particularly in isolating vocals, drums, and bass elements from mixed tracks. These developments owe m…
Pre-trained Spatial Priors on Multichannel NMF for Music Source Separation
Pablo Cabanas-Molero, Antonio J. Munoz-Montoro, Julio Carabias-Orti +1
This paper presents a novel approach to sound source separation that leverages spatial information obtained during the recording setup. Our method trains a spatial mixing filter us…
Multichannel Singing Voice Separation by Deep Neural Network Informed DOA Constrained CNMF
Antonio J. Muñoz-Montoro, Julio J. Carabias-Orti, Archontis Politis +1
This work addresses the problem of multichannel source separation combining two powerful approaches, multichannel spectral factorization with recent monophonic deep-learning (DL) b…